Identification of refractive index for label-free single cells based on 2D light scattering technology

Xu, Rui; Zhang, Jin; Wei, Xiangyu; Sun, Xuming; Xie, Li

doi:10.1117/12.2642245

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Figure 1 illustrates the configuration of our 2D light scattering microscopy, which closely resembles our previously documented system 13 . The key components of the device encompass a Diode-pumped laser (DPSSL, Edmund Optics, USA), objectives, a fiber optic probe, a liquid-based chip, and a CMOS detector.…”

Section: Experimental Materials and Devices 21 Setup Of The 2d Light ...mentioning

confidence: 68%

Identification of lymphoma types using 2D light scattering microscopy and machine learning

Xu,

Zhang,

Chen

et al. 2023

Optics in Health Care and Biomedical Optics XIII

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Lymphomas encompass Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL). Accurate clinical diagnosis is paramount for informed treatment and prognosis, and lymphoma classification plays a central role in guiding treatment strategies and evaluating treatment outcomes. In this study, we have developed a static cytometry leveraging laser and microscope technology to capture two-dimensional (2D) light scattering patterns of individual cells. Within this method, a single lymphoma cell is positioned in a liquid-based chip and vertically stimulated by a 532 nm green laser. The resulting light scattering pattern of the cell is observed and recorded by a COMS detector through a microscope optical system, covering a polar angle range of 75 to 105 degrees. The light scattering pattern exhibited by lymphoma cells displays distinct speckles, with the texture features of these speckles influenced by internal cell structures, including organelle count and their spatial arrangement. By extracting and analyzing the characteristic values from these cell scattering patterns, we can achieve lymphoma cell identification. In this study, we successfully differentiated between HDLM-2 cells (HL) and Daudi cells (NHL) using the machine learning support vector machine (SVM) algorithm, achieving a classification accuracy of 88%. This outcome underscores the potential of our 2D light scattering static cytometry for lymphoma cell classification, offering a marker-free, cost-effective approach for early cancer screening at the single-cell level.

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Section: Experimental Materials and Devices 21 Setup Of The 2d Light ...mentioning

confidence: 68%

Identification of lymphoma types using 2D light scattering microscopy and machine learning

Xu,

Zhang,

Chen

et al. 2023

Optics in Health Care and Biomedical Optics XIII

Self Cite

View full text Add to dashboard Cite

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Multimodal detection system for lymphoma integrating 2D light scattering and electrochemical techniques

Zhang,

Yang,

Wang

et al. 2024

Optics in Health Care and Biomedical Optics XIV

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Lymphoma has become one of the most prevalent malignant tumors in China, with a significant rise in incidence among young people in recent years. Early diagnosis and treatment are therefore crucial for improving patient outcomes, including efficacy, survival, and quality of life. In this study, we developed a multimodal detection system that combines twodimensional (2D) light scattering and electrochemical techniques to differentiate between normal and tumor cells at the single-cell and molecular level. Using a laser microscopy detection system, we capture 2D light scattering images of individual cells, where the lymphocytes display distinctive patch-like patterns. The texture of these patterns is influenced by the internal cellular structures, and the differentiation of normal and tumor cells is achieved by extracting and analyzing the eigenvalues from the light scattering images. Additionally, electrochemical sensors detect hydrogen peroxide levels in the cellular solution by measuring changes in current, with tumor cells producing a greater current variation than normal cells. A support vector machine (SVM) algorithm was employed to distinguish between normal and tumor cells, achieving an accuracy of 88%. The results demonstrate that the multimodal detection system effectively differentiates normal and tumor cells from both physical and chemical perspectives, enhancing detection accuracy. This system offers a nondestructive, efficient, and cost-effective method for early cancer screening.

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Identification of refractive index for label-free single cells based on 2D light scattering technology

Cited by 2 publications

References 20 publications

Identification of lymphoma types using 2D light scattering microscopy and machine learning

Identification of lymphoma types using 2D light scattering microscopy and machine learning

Multimodal detection system for lymphoma integrating 2D light scattering and electrochemical techniques

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